Vehicle power battery fault diagnosis method based on layered identification

Abstract

The invention belongs to the technical field of power battery safety, and particularly relates to a vehicle power battery fault diagnosis method based on hierarchical recognition, which comprises the following steps: acquiring actual operation data of a vehicle; dividing the actual operation data into a plurality of data fragments, and extracting one-dimensional or multi-dimensional characteristic quantities from the data fragments; screening out abnormal data fragments from all the data fragments according to the characteristic quantities of the data fragments; and for the abnormal data fragments, calculating a longitudinal abnormal average value of the voltage of the power battery monomer to identify the suspected faulty battery monomer in the power battery, and analyzing the fault type of the power battery according to the performance of the suspected faulty battery monomer. According to the method, the abnormal segments are screened out by analyzing the actual operation data of the vehicle, whether the battery breaks down or not is judged, and the suspected faulted single battery in the power battery can be recognized, so that the battery fault type and fault reason can be analyzed subsequently, and the safety of the vehicle and personnel is maintained.

Description

Technical Field

[0001] The present invention belongs to the technical field of power battery safety, and in particular relates to a vehicle power battery fault diagnosis method based on hierarchical identification. Background Art

[0002] In the process of comprehensive electrification of transportation, power batteries are one of the core components of electric vehicles (including passenger cars, buses, trucks, etc.), and their performance directly affects the economy and safety of electric vehicles. However, due to the aging of the power battery itself, the failure of sensor acquisition or other external reasons, when the electric vehicle is stopped from charging or driving, there will be a variety of fault problems such as connection failure, sampling failure, insulation failure, self-discharge failure and sudden internal short circuit failure. For example, in actual applications, due to the internal mechanical loss of control of the power battery or the external collision and extrusion during application, the heat accumulation speed of the battery is greater than the diffusion speed, which in turn causes a fire accident that burns quickly and is difficult to extinguish, endangering personal safety.

[0003] In order to better identify power battery faults during operation and determine the fault type, it is necessary to conduct targeted analysis based on the actual operating data of the vehicle. Summary of the invention

[0004] In view of the shortcomings of the prior art described above, an object of the present invention is to provide a fault diagnosis method that can solve the problem of power battery fault identification and diagnosis based on the actual operating data of an electric vehicle powered by a power battery, so as to improve the safety and economic benefits of the electric vehicle.

[0005] To achieve the above-mentioned purpose and other related purposes, the present invention provides a vehicle power battery fault diagnosis method based on hierarchical identification, including: acquiring actual operating data of the vehicle; dividing the actual operating data into a plurality of data segments, and extracting one-dimensional or multi-dimensional feature quantities from the data segments; screening out abnormal data segments from all data segments according to the feature quantities of the data segments; for abnormal data segments, calculating the longitudinal abnormal average value of the power battery cell voltage to identify the battery cells suspected of being faulty in the power battery, and analyzing the fault type of the power battery based on the performance of the battery cells suspected of being faulty.

[0006] According to a specific embodiment of the present invention, after obtaining the actual operation data of the vehicle, the method further includes: performing data cleaning on the actual operation data to remove failure values ​​in the original data.

[0007] According to a specific embodiment of the present invention, the steps of dividing the actual operation data into a plurality of data segments, and extracting one-dimensional or multi-dimensional feature quantities from the data segments include: for any data segment, extracting the corresponding multi-dimensional feature quantities according to the sampling parameters about the power battery therein; wherein the sampling parameters of the power battery include: the total voltage of the power battery, the total current of the power battery, the state of charge of the power battery, the temperature extreme of the power battery, the insulation resistance of the power battery, and the single cell voltage of the power battery.

[0008] According to a specific embodiment of the present invention, for any data segment, the step of extracting corresponding multi-dimensional feature quantities based on the sampling parameters of the power battery therein includes: for the total voltage, total current, state of charge, or insulation resistance of the power battery, obtaining the sampling values ​​at all times, and calculating the average value, standard deviation, quartile, quartile, minimum value and maximum value of the sampling values ​​as one of the one-dimensional feature quantities; for the temperature extremes of the power battery, obtaining the sampling values ​​at all times, and calculating the minimum value, maximum value, and average value of the lowest sampling temperature, and the minimum value, maximum value, and average value of the highest sampling temperature, as one of the one-dimensional feature quantities; for the single cell voltage of the power battery, obtaining the sampling values ​​at all times, and calculating the maximum value and standard deviation of the highest single cell voltage, as well as the average value and standard deviation of the single cell voltage extreme difference, and the average value and maximum value of the voltage entropy, as one of the one-dimensional feature quantities.

[0009] According to a specific embodiment of the present invention, the step of filtering out abnormal data fragments from all data fragments based on the feature quantities of the data fragments includes: for any two data fragments, calculating the Euclidean distance between different data fragments with respect to the same feature quantities, and adding one to the number of abnormalities of the two data fragments when the Euclidean distance is abnormal; identifying abnormal data fragments based on the cumulative number of abnormalities of each of the data fragments, and integrating all abnormal data fragments to diagnose whether the power battery is faulty.

[0010] According to a specific embodiment of the present invention, the step of judging whether the Euclidean distance is abnormal includes: for any type of feature quantity, based on all data segments, calculating the Euclidean distance of any two different data segments with respect to the feature quantity, until all Euclidean distances with respect to the feature quantity are obtained; according to all Euclidean distances with respect to the feature quantity, the corresponding quartile, quartile, and standard deviation are calculated, and according to the quartile, the quartile, and the standard deviation, a first threshold and a second threshold are calculated to serve as a reference standard for whether the Euclidean distance of the feature quantity is abnormal; wherein, when the Euclidean distance is lower than the corresponding first threshold or higher than the corresponding second threshold, the Euclidean distance is considered to be abnormal.

[0011] According to a specific embodiment of the present invention, the calculation formulas of the first threshold and the second threshold are as follows:

[0012] x1=Q1-1.5*Std,

[0013] x2=Q2+1.5*Std,

[0014] Among them, x1 represents the first threshold, x2 represents the second threshold, Q1 represents the quartile, Q2 represents the quartile, and Std represents the standard deviation.

[0015] According to a specific embodiment of the present invention, for abnormal data segments, the longitudinal abnormal average value of the power battery cell voltage is calculated to identify the battery cell suspected of failure in the power battery, and the steps of analyzing the fault type of the power battery according to the performance of the battery cell suspected of failure include: for each abnormal data segment, it is divided into a number of charging segments and discharging segments: the longitudinal abnormal average value of each cell of the power battery within each of the charging segment / discharging segment is calculated, and when the longitudinal abnormal average value is abnormal, the abnormal number of the corresponding cell is increased by one; the battery cell suspected of failure is identified according to the cumulative number of abnormalities of each cell of the power battery.

[0016] According to a specific embodiment of the present invention, the calculation formula of the longitudinal abnormal average value is as follows:

[0017]

[0018] Among them, LOA j It represents the average value of the longitudinal abnormality of the jth cell of the power battery, V ij represents the cell voltage of the jth cell at the i-th time, V imid It represents the median of all cell voltages at the i-th moment, and n represents the number of cells in the power battery.

[0019] According to a specific embodiment of the present invention, the step of determining whether the longitudinal abnormal average value is abnormal includes: for each charging segment / discharging segment, using the three sigma law to determine a corresponding standard interval; comparing the longitudinal abnormal average value calculated using the charging segment / discharging segment with the standard interval: if the longitudinal abnormal average value exceeds the standard interval, the longitudinal abnormal average value is considered to be abnormal.

[0020] The present invention provides a vehicle power battery fault diagnosis method based on hierarchical identification, which can quickly identify faults in faulty vehicles that only contain data for several months, screen out abnormal fragments by analyzing actual operating data, and determine whether the vehicle's power battery has a fault. At the same time, it can further identify suspected faulty battery cells in the power battery, so as to facilitate subsequent specific analysis of the battery fault type and the cause of the fault. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 A schematic flow chart of a specific embodiment of a vehicle power battery fault diagnosis method based on hierarchical identification provided by the present invention;

[0022] Figure 2 A schematic structural diagram of a specific embodiment of a vehicle power battery fault diagnosis system based on hierarchical identification provided by the present invention;

[0023] Figure 3 The present invention provides a structural block diagram of a specific embodiment of an electronic device. DETAILED DESCRIPTION

[0024] The following will describe the embodiments of the present invention with reference to the accompanying drawings and preferred embodiments. Those skilled in the art can easily understand other advantages and effects of the present invention from the contents disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and the details in this specification can also be modified or changed in various ways based on different viewpoints and applications without departing from the spirit of the present invention. It should be understood that the preferred embodiments are only for illustrating the present invention, not for limiting the scope of protection of the present invention.

[0025] It should be noted that the illustrations provided in the following embodiments are only schematic illustrations of the basic concept of the present invention, and thus the drawings only show components related to the present invention rather than being drawn according to the number, shape and size of components in actual implementation. In actual implementation, the type, quantity and proportion of each component may be changed arbitrarily, and the component layout may also be more complicated.

[0026] In the following description, numerous details are discussed to provide a more thorough explanation of the embodiments of the present invention. However, it is obvious to those skilled in the art that the embodiments of the present invention can be implemented without these specific details. In other embodiments, structures and devices known to the public are shown in the form of block diagrams rather than in detail to avoid making the embodiments of the present invention difficult to understand.

[0027] Example 1

[0028] See also Figure 1As shown, a vehicle power battery fault diagnosis method based on hierarchical identification includes:

[0029] Step S100, obtaining actual operation data of the vehicle.

[0030] It should be noted that the actual operation data mentioned in this embodiment refers to the data collected / detected by relevant vehicle sensors or other devices when the electric vehicle is started or running using the power battery. For example, when the vehicle is driving on the road, other sampling parameters such as the output current change, output voltage change, and temperature change of the power battery will be collected accordingly. As the heart of the electric vehicle, in order to ensure the driving safety of the vehicle, it is necessary to focus on whether the power battery can work normally and whether there are any abnormalities or failures.

[0031] The fault diagnosis method provided in this embodiment can diagnose the fault status of the power battery online, and of course can also be used for offline diagnosis. For example, after the vehicle is started, the relevant vehicle sensors will collect the working parameters of the power battery in real time and feed them back to the vehicle terminal. Then, the vehicle terminal can perform fault diagnosis based on the fed-back sampling parameters, so as to monitor the operating status of the power battery in real time and ensure the safety of the vehicle and personnel; or, after the vehicle has been running for a period of time, the corresponding data can be integrated to perform fault diagnosis regularly to feed back the health status of the power battery.

[0032] It should also be added that the actual operating data mentioned in this embodiment is not just the operating data of the power battery at a certain moment, but the operating data of the power battery over a period of time. By specifically analyzing the sampling parameters of the power battery over a period of time, it is possible to effectively identify whether the power battery has a fault, the type of fault, etc.

[0033] Based on the above, after obtaining the actual operation data of the vehicle, the data can be preprocessed, such as denoising, filtering, etc., without too much restriction. The modifications and modifications made by those skilled in the art to the embodiments of the present invention without departing from the spirit of the present invention still fall within the scope of the invention application of the present invention. In this embodiment, in order to simplify the data processing steps, it is preferred to perform only simple data cleaning, that is, to remove the failure values ​​in the original data, thereby ensuring that the subsequent fault diagnosis of the power battery is not affected by some abnormal parameters.

[0034] Step S200: divide the actual operation data into a plurality of data segments, and extract one-dimensional or multi-dimensional feature quantities from the data segments.

[0035] After the data cleaning is completed, the actual operation data can be divided into several data segments, for example, according to the vehicle's mileage, time, or preset segment length. It can be understood here that since the actual operation data is the operation data of the vehicle's power battery over a period of time, it can be continuous or discontinuous, and can be arranged in chronological order. If it is split according to the vehicle's mileage, then the data before and after the vehicle travels the preset mileage can be split into a data segment, thereby obtaining several data segments; if it is split according to time, then the data of the vehicle within half a day, a day or a few days can be split into a data segment, thereby obtaining several data segments; if it is split according to the segment length, then all the data can be split into several data segments of equal length, and it can also be split into data segments of unequal length, etc. There are no excessive restrictions on how to split the data and the number of data segments.

[0036] Secondly, it is necessary to extract the corresponding feature quantity for each data segment. It should be noted here that in order to better illustrate the technical solution of the present invention, in this embodiment, the sampling parameters of the power battery are taken as total voltage, total current, state of charge (SOC), temperature extremes, insulation resistance, and single cell voltage, that is, the vehicle sensor or other device needs to obtain the relevant parameters of the total voltage, total current, SOC, temperature extremes, insulation resistance, and single cell voltage of the power battery during operation to monitor the operating state of the power battery, and feed it back to the vehicle terminal for storage. Therefore, each data segment of the actual operation data contains sampling parameters of the total voltage, total current, SOC, temperature extremes, insulation resistance, and single cell voltage of the power battery, and each data segment needs to extract the corresponding feature quantity for different sampling parameters, that is, for any data segment, 6-dimensional feature quantities need to be extracted. However, it can be understood that in actual applications, the sampling parameters of the power battery are not limited to the above, and the sampling parameters can be added or reduced according to the actual situation and needs, and the corresponding feature quantities need to be adaptively adjusted when extracting the feature quantities to obtain the corresponding one-dimensional or multi-dimensional feature quantities.

[0037] In a specific embodiment, for the total voltage, total current, SOC and insulation resistance of the power battery, the average value, standard deviation, quartile, quartile, minimum value and maximum value of the corresponding sampling values ​​in the segment can be extracted, totaling 6 feature quantities, that is, a data segment will contain multiple sampling moments, by obtaining the sampling values ​​of the total voltage, total current, SOC and insulation resistance at all sampling moments, and calculating the average value, standard deviation, quartile, quartile, minimum value and maximum value of all sampling values ​​of the total voltage, the average value, standard deviation, quartile, quartile, minimum value and maximum value of all sampling values ​​of the total current, the average value, standard deviation, quartile, quartile, minimum value and maximum value of all sampling values ​​of the SOC, and the average value, standard deviation, quartile, quartile, minimum value and maximum value of all sampling values ​​of the insulation resistance, thereby constituting the total voltage, total current, SOC and insulation resistance distribution of the power battery in the segment.

[0038] For the temperature extremes of the power battery, the sampling values ​​of all sampling moments are also obtained, and the minimum, maximum, and average values ​​of the lowest sampling temperature, as well as the minimum, maximum, and average values ​​of the highest sampling temperature, are calculated, totaling 6 feature quantities, thereby constituting the temperature extreme value distribution of the power battery in this segment. It should be noted here that the sampling of temperature can be a corresponding distribution change, that is, the temperature change of the power battery within a certain period of time, as a sampling value. For example, the sampling of temperature at a sampling moment can be a temperature distribution change between the previous moment and the current moment. And there must be a maximum value and a minimum value in a distribution change, that is, corresponding to the highest sampling temperature and the lowest sampling temperature, respectively. Therefore, the highest sampling temperature and the lowest sampling temperature in all sampling values ​​can be collected, and then the minimum value, maximum value, and average value of all sampling lowest temperatures, as well as the minimum value, maximum value, and average value of all sampling highest temperatures can be calculated.

[0039] For the single cell voltage of the power battery, the sampling values ​​at all sampling moments are obtained, and the voltage distribution changes of the corresponding power battery single cells within a certain period of time can be calculated, and then the maximum value and standard deviation of the single cell maximum voltage, as well as the average value and standard deviation of the single cell voltage range can be calculated. At the same time, the voltage entropy at each sampling moment in the segment can also be calculated, and the average value and maximum value of the voltage entropy at all sampling moments can be calculated, a total of 6 feature quantities, thus forming the single cell voltage distribution of the power battery in this segment.

[0040] Based on the above, corresponding 6-dimensional feature quantities can be extracted from each data segment to represent the operating status of the power battery. At the same time, since each dimension contains 6 feature quantities and there are 6 dimensions in total, a 6*6 feature matrix can be obtained in the end, which fully reflects the status performance of the power battery in this segment.

[0041] It should be noted that there are no excessive restrictions on the feature quantity of each dimension. The feature quantity can be increased or decreased according to the actual situation and needs, and is not limited to the above 6 types. There are no excessive restrictions on this. The modifications and improvements made to the embodiments of the present invention by those skilled in the art without departing from the spirit of the present invention still fall within the scope of the invention application of the present invention.

[0042] Step S300: Screen out abnormal data segments from all data segments according to the feature quantities of the data segments.

[0043] Furthermore, by measuring the similarity between two data fragments, abnormal data fragments can be screened out. Specifically, by calculating the Euclidean distance of each feature quantity between the two data fragments, the similarity of the distribution of sampling values ​​between the independent fragments is measured, and the larger the distance, the lower the similarity. It should be noted here that the calculation of the Euclidean distance between each feature quantity specifically refers to the calculation between the same type of feature quantities, that is, the Euclidean distance between the feature quantity of the total voltage of the power battery of one data fragment and the feature quantity of the total voltage of the power battery of another data fragment, and all feature quantities need to be calculated one by one. Therefore, when there are multiple data fragments, any two data fragments can be matched by permutations and combinations, and the Euclidean distance of each feature quantity between the two data fragments can be calculated.

[0044] In addition, it is also necessary to identify whether the calculated Euclidean distance meets the standard. First, based on any type of feature quantity, such as the total voltage, total current, SOC, temperature extreme value, insulation resistance, and single cell voltage of the power battery, the Euclidean distance of any two data fragments is calculated based on all the data fragments, so that several calculated values ​​can be obtained. Secondly, the corresponding quartiles, quartiles, and standard deviations are calculated based on all the calculated values, which are recorded as Q1, Q2, and Std respectively. Then, the first threshold x1 and the second threshold x2 are calculated based on Q1, Q2, and Std, that is, x1 = Q1-1.5*Std, x2 = Q2+1.5*Std, as the judgment standard for the abnormal Euclidean distance of this type. Finally, when any two data fragments calculate the Euclidean distance of this type of feature quantity, if the calculated Euclidean distance is lower than the first threshold x1 = Q1-1.5*Std or higher than the second threshold x2 = Q2+1.5*Std, it is considered abnormal, and the number of abnormalities of the two data fragments is recorded once each. By analogy, the Euclidean distance of this type of feature quantity can be calculated for all data fragments after permutation and combination for identification.

[0045] Similarly, other types of feature quantities also use the above method to calculate the Euclidean distance and identify whether they are abnormal. Finally, the cumulative number of abnormalities of all data segments is counted to filter out abnormal data segments. In practical applications, for example, when the cumulative number of abnormalities exceeds one-third of the total number of data segments, the data segment is considered to be abnormal. Similarly, there is no need to limit the standard for determining abnormalities, and it can be adjusted according to actual conditions and needs.

[0046] Based on the above, it is possible to preliminarily diagnose whether the power battery is faulty based on the abnormal data segments. It should be noted here that the above data segments can be further classified when recording the cumulative number of abnormalities, such as which type of feature quantity has the most abnormal Euclidean distances in a certain data segment, or the distribution of abnormal number of Euclidean distances of all types of feature quantities, so that the number of abnormal Euclidean distances of different types of feature quantities can be counted, and the fault type and cause of the power battery can be analyzed more accurately.

[0047] In a specific embodiment, for example, whether the insulation of a power battery fails can be determined based on all abnormal data segments. When the ratio of the abnormal number of Euclidean distances of insulation resistance to the abnormal number of Euclidean distances of total voltage does not exceed 0.6, it is considered that the abnormal cause of the Euclidean distance of the insulation resistance type feature quantity is sampling abnormality. Otherwise, it is considered that the fluctuation of the insulation resistance of the power battery is related to its total voltage, and the insulation fails. The abnormal temperature of the power battery can also be determined based on the abnormal data segments. When the ratio of the abnormal number of Euclidean distances of temperature extreme values ​​to the abnormal number of Euclidean distances of total current does not exceed 0.6, it is considered that the abnormal cause of the Euclidean distance of the temperature extreme value type feature quantity is sampling abnormality, that is, it has nothing to do with the working condition of the power battery. Otherwise, it is related to the working condition and is not caused by sampling abnormality, but the operating state of the power battery does not meet the normal standard.

[0048] It can be seen that abnormal data fragments can be used to preliminarily diagnose whether the power battery is operating normally, so that early warning can be given to avoid endangering the safety of vehicles and personnel.

[0049] Step S400 , for the abnormal data segment, by calculating the longitudinal abnormal average value of the power battery cell voltage, to identify the battery cell suspected of failure in the power battery, and analyze the failure type of the power battery according to the performance of the battery cell suspected of failure.

[0050] Based on the abnormal data segments obtained above, fault identification can be further performed to obtain specific faulty cells, that is, battery cells (cells) that may fail in the power battery. Specifically, first, the abnormal data segments need to be divided into several charging segments and discharging segments. Secondly, the specific faulty cells are identified by calculating the Longitudinal Outlier Average (LOA) of the cell voltage of the power battery. Specifically, for all charging segments and discharging segments, the Longitudinal Outlier Average LOA of each battery cell of the power battery within each segment is calculated. The calculation formula is as follows:

[0051]

[0052] Among them, LOA j It represents the average value of the longitudinal abnormality of the jth cell of the power battery, V ij represents the cell voltage of the jth cell at the i-th time, V imid It represents the median of all cell voltages at the i-th moment, and n represents the number of cells in the power battery.

[0053] Furthermore, according to the three-sigma principle, the reliable interval of the LOA value of each segment, that is, the standard interval, is obtained to measure whether the LOA value is normal. It should be noted here that the LOA value of the battery cell calculated by any charging segment / discharging segment can only be compared with the reliable interval determined by the current segment, and when a LOA value of a battery cell is abnormal, that is, the LOA value exceeds the corresponding reliable interval, the number of abnormal times of the battery cell is recorded once, and finally the cumulative number of abnormal times of all battery cells can be statistically obtained. The more times, the higher the degree of suspected failure of the battery cell. The specific evaluation criteria can be determined according to the actual situation and needs, and there are no excessive restrictions on this.

[0054] Finally, after determining the specific faulty unit, the corresponding fault characteristics can be extracted for diagnosis based on the occurrence principles of different fault types, including typical fault types such as abnormal self-discharge and internal short circuit. Without making too many restrictions on this, the modifications and improvements made to the embodiments of the present invention by those skilled in the art without departing from the spirit of the present invention still fall within the scope of the invention patent application of the present invention.

[0055] In a specific embodiment, the connection fault of the power battery can be judged by the voltage difference between adjacent cells. The specific fault feature extraction method and type identification method are as follows:

[0056] Extraction of voltage differences between adjacent cells: Based on all the above-mentioned charging and discharging segments, analyze the voltage inconsistency of the suspected faulty cell. After confirming the cell number of the suspected faulty cell, select the voltage data of the two adjacent battery cells before and after the suspected faulty cell to compare the fault performance. Take the average of the voltage data of the four adjacent battery cells, and subtract the voltage average from the voltage of the suspected faulty cell. The voltage inconsistency can be quantified by the difference.

[0057] Fault type identification: According to the voltage difference characteristics of adjacent cells, 5mV is used as the difference threshold to determine the fault time point. Analyze the difference performance of the charging segment and the discharging segment after the fault time point respectively. According to the formation mechanism of the connection fault, when a battery cell has a connection fault, the voltage of the faulty battery cell is lower than that of the other battery cells in the discharge behavior, and the voltage of the faulty battery cell is higher than that of the other battery cells in the charging behavior. According to the voltage difference characteristics of adjacent cells, check whether the charging and discharging segments after the fault time point are greater than or less than 0. Voltage spike check: Compare the voltage of the suspected faulty cell with the total current of the power battery, and check whether the voltage spike of the suspected faulty cell appears at the same frequency as the total current spike, so as to confirm whether the voltage mutation of the suspected faulty battery cell is caused by high-current charging due to kinetic energy recovery during vehicle driving.

[0058] For self-discharge anomalies, the power battery SOC and the voltage change of the faulty cell are jointly analyzed to determine whether it is a self-discharge anomaly. The specific steps are as follows:

[0059] The voltage data of the two adjacent battery cells before and after the faulty cell are selected for fault performance comparison. The voltage data of the four adjacent battery cells are averaged, and the voltage average is subtracted from the voltage of the suspected faulty cell. The voltage inconsistency can be quantitatively characterized by the difference. The voltage inconsistency difference obtained by the battery cell is jointly analyzed with the overall SOC of the power battery to determine the change trend of the difference when the vehicle is at high SOC and low SOC, and calibrate it. According to the characteristics of the voltage difference of adjacent cells, 5mV is used as the difference threshold to determine the fault time point. Analysis of the SOC and voltage change trends after the fault time point: According to the formation mechanism of self-discharge fault, when the battery cell has a self-discharge fault, the high SOC will increase the inconsistency of the battery cell. In the high SOC operating range (SOC>60%), the absolute value of the adjacent voltage difference is within 15mV to 25mV; in the low SOC operating range (SOC<60%), the absolute value of the adjacent voltage difference is between 0mV and 15mV.

[0060] For sudden internal short-circuit faults, by calculating the voltage difference between adjacent cells and the first-order differential variance of the cell voltage, combined with the battery temperature change trend, determine whether it is a sudden internal short-circuit fault. For the voltage difference between adjacent cells, it can be calculated in the same way as the extraction method of the connection fault feature, which will not be described in detail. For the first-order differential variance feature of the cell voltage: First, calculate the first-order difference of each cell voltage to characterize the rate of change of the battery cell; calculate the cell voltage differential variance at all sampling moments in each abnormal segment. The larger the differential variance, the more asynchronous the cell voltage changes in the power battery. Combine the above two characteristic change trends with the temperature change trend for comparison. If a sudden temperature change occurs that is greater than a certain threshold, it is determined that a sudden internal short-circuit fault has occurred.

[0061] It can be seen that the fault diagnosis method provided by the present application can be used to diagnose whether the vehicle power battery is operating normally or in a fault condition, and can realize the early warning function of the power battery, thereby maintaining the safety of the vehicle and personnel. At the same time, the fault type and cause of the power battery can be further analyzed to facilitate battery maintenance.

[0062] It should be noted that the step division of the above methods is only for clear description. When implemented, they can be combined into one step or some steps can be split and decomposed into multiple steps. As long as they contain the same logical relationship, they are all within the scope of protection of this application; adding insignificant modifications to the algorithm or process or introducing insignificant designs without changing the core design of the algorithm and process are all within the scope of protection of this application.

[0063] Example 2

[0064] See also Figure 2 As shown, the embodiment of the present application also provides a vehicle power battery fault diagnosis system based on hierarchical identification, including:

[0065] The data collection module 10 is used to obtain the actual operation data of the vehicle.

[0066] The feature extraction module 20 is used to divide the actual operation data into a plurality of data segments and extract one-dimensional or multi-dimensional feature quantities from the data segments.

[0067] The abnormality judgment module 30 is used to filter out abnormal data segments from all data segments according to the feature quantities of the data segments.

[0068] The fault identification module 40 calculates the longitudinal abnormal average value of the power battery cell voltage for the abnormal data segment to identify the battery cell suspected of having a fault in the power battery, and analyzes the fault type of the power battery according to the performance of the battery cell suspected of having a fault.

[0069] It should be noted that the vehicle power battery fault diagnosis system based on hierarchical identification provided in the above embodiment and the vehicle power battery fault diagnosis method based on hierarchical identification provided in the above embodiment 1 belong to the same concept, wherein the specific manner in which each module and unit performs the operation has been described in detail in the method embodiment, and will not be repeated here. In actual application, the vehicle power battery fault diagnosis method based on hierarchical identification provided in the above embodiment 1 can allocate the above functions to different functional modules as needed, that is, divide the internal structure of the device into different functional modules to complete all or part of the functions described above, and this is not limited here.

[0070] Example 3

[0071] See also Figure 3 As shown, an embodiment of the present application further provides an electronic device, comprising a memory 2, a processor 1, and a program stored in the memory and executable on the processor, wherein when the processor executes the program, the steps of any one of the above methods are implemented.

[0072] Among them, the memory includes at least one type of readable storage medium, and the readable storage medium includes flash memory, mobile hard disk, multimedia card, card-type memory (for example: SD or DX memory, etc.), magnetic memory, disk, optical disk, etc. The memory can be an internal storage unit of an electronic device in some embodiments, such as a mobile hard disk of the electronic device. The memory can also be an external storage device of an electronic device in other embodiments, such as a plug-in mobile hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), etc. equipped on the electronic device. Further, the memory can also include both an internal storage unit of the electronic device and an external storage device. The memory can be used not only to store application software and various types of data installed in the electronic device, but also to temporarily store data that has been output or is to be output.

[0073] In some embodiments, the processor may be composed of an integrated circuit, for example, a single packaged integrated circuit, or a plurality of packaged integrated circuits with the same or different functions, including one or more central processing units (CPUs), microprocessors, digital processing chips, graphics processors, and a combination of various control chips. The processor is the control core (Control Unit) of the electronic device, and uses various interfaces and lines to connect the various components of the entire electronic device, and executes various functions of the electronic device and processes data by running or executing programs or modules stored in the memory, and calling data stored in the memory.

[0074] The processor executes the operating system of the electronic device and various installed application programs. The processor executes the application programs to implement the steps in the above method embodiment.

[0075] Exemplarily, the program may be divided into one or more modules, which are stored in the memory and executed by the processor to complete the present invention. The one or more modules may be a series of program instruction segments capable of completing specific functions, which are used to describe the execution process of the program in the electronic device.

[0076] The above-mentioned integrated unit implemented in the form of software function modules can be stored in a computer-readable storage medium. The above-mentioned software function modules are stored in a storage medium and include several instructions for enabling a computer device (which can be a personal computer, a computer device, or a network device, etc.) or a processor to perform part of the functions of various embodiments of the present invention.

[0077] In summary, the present invention provides a vehicle power battery fault diagnosis method based on hierarchical identification, which can quickly identify faults in faulty vehicles that only contain data for several months, filter out abnormal fragments by analyzing actual operating data, and determine whether the vehicle's power battery has a fault. At the same time, it can further identify suspected faulty battery cells in the power battery, so as to facilitate the subsequent specific analysis of the battery fault type and the cause of the fault.

[0078] The above embodiments are merely illustrative of the principles and effects of the present invention, and are not intended to limit the present invention. Anyone familiar with the technology may modify or change the above embodiments without violating the spirit and scope of the present invention. Therefore, all equivalent modifications or changes made by a person of ordinary skill in the art without departing from the spirit and technical ideas disclosed by the present invention shall still be covered by the claims of the present invention.

Claims

1. A vehicle power battery fault diagnosis method based on hierarchical identification, characterized in that: include: Obtain the actual operation data of the vehicle; Dividing the actual operation data into a plurality of data segments, and extracting one-dimensional or multi-dimensional feature quantities from the data segments; Screening out abnormal data segments from all data segments according to the feature quantities of the data segments; For abnormal data segments, the longitudinal abnormal average value of the power battery cell voltage is calculated to identify the battery cells suspected of failure in the power battery, and the fault type of the power battery is analyzed based on the performance of the battery cells suspected of failure.

2. The vehicle power battery fault diagnosis method based on hierarchical identification according to claim 1 is characterized in that: After obtaining the actual operation data of the vehicle, it also includes: The actual operation data is cleaned to remove failure values ​​in the original data.

3. The vehicle power battery fault diagnosis method based on hierarchical identification according to claim 1 is characterized in that: The steps of dividing the actual operation data into a plurality of data segments and extracting one-dimensional or multi-dimensional feature quantities from the data segments include: For any data segment, extract the corresponding multi-dimensional feature quantity according to the sampling parameters of the power battery therein; The sampling parameters of the power battery include: the total voltage of the power battery, the total current of the power battery, the state of charge of the power battery, the temperature extreme value of the power battery, the insulation resistance of the power battery, and the single cell voltage of the power battery.

4. The vehicle power battery fault diagnosis method based on hierarchical identification according to claim 3 is characterized in that: For any data segment, the step of extracting corresponding multi-dimensional feature quantities according to the sampling parameters of the power battery therein includes: For the total voltage, total current, state of charge, or insulation resistance of the power battery, obtain the sampling values ​​at all times, and calculate the average value, standard deviation, quartile, quartile, minimum value, and maximum value of the sampling values ​​as one of the one-dimensional feature quantities; For the temperature extremes of the power battery, the sampling values ​​at all times are obtained, and the minimum value, maximum value, and average value of the lowest sampled temperature, as well as the minimum value, maximum value, and average value of the highest sampled temperature are calculated as one-dimensional feature quantities; For the single cell voltage of the power battery, the sampling values ​​at all times are obtained, and the maximum value and standard deviation of the single cell highest voltage, the average value and standard deviation of the single cell voltage range, and the average value and maximum value of the voltage entropy are calculated as one of the one-dimensional feature quantities.

5. The vehicle power battery fault diagnosis method based on hierarchical identification according to claim 1 is characterized in that: The step of screening out abnormal data segments from all data segments according to the feature quantities of the data segments comprises: For any two data segments, calculate the Euclidean distances between different data segments with respect to the same type of feature quantities, and when the Euclidean distance is abnormal, add one to the abnormal times of the two data segments; The abnormal data segments are identified according to the accumulated number of abnormalities of each of the data segments, and all the abnormal data segments are integrated to diagnose whether the power battery is faulty.

6. The vehicle power battery fault diagnosis method based on hierarchical identification according to claim 5 is characterized in that: The steps to determine the Euclidean distance anomaly include: For any type of feature quantity, the Euclidean distance between any two different data segments with respect to the feature quantity is calculated based on all data segments, until all Euclidean distances with respect to the feature quantity are obtained; According to all the Euclidean distances about the feature quantity, a corresponding quartile, a quartile, and a standard deviation are calculated, and according to the quartile, the quartile, and the standard deviation, a first threshold and a second threshold are calculated to serve as a reference standard for whether the Euclidean distance of the feature quantity is abnormal; When the Euclidean distance is lower than the corresponding first threshold or higher than the corresponding second threshold, the Euclidean distance is considered abnormal.

7. The vehicle power battery fault diagnosis method based on hierarchical identification according to claim 6 is characterized in that: The calculation formulas of the first threshold and the second threshold are as follows: x1=Q1-1.5*Std, x2=Q2+1.5*Std, Among them, x1 represents the first threshold, x2 represents the second threshold, Q1 represents the quartile, Q2 represents the quartile, and Std represents the standard deviation.

8. The vehicle power battery fault diagnosis method based on hierarchical identification according to claim 1 is characterized in that: For the abnormal data segments, the steps of calculating the longitudinal abnormal average value of the power battery cell voltage to identify the battery cell suspected of failure in the power battery, and analyzing the failure type of the power battery according to the performance of the battery cell suspected of failure include: For each abnormal data segment, it is divided into several charging segments and discharging segments: Calculate the longitudinal abnormal average value of each cell of the power battery during each charging segment / discharging segment, and when the longitudinal abnormal average value is abnormal, add one to the abnormal number of the corresponding cell; Identify suspected faulty battery cells based on the cumulative number of abnormalities of each power battery cell.

9. The vehicle power battery fault diagnosis method based on hierarchical identification according to claim 8 is characterized in that: The calculation formula for the longitudinal anomaly mean is as follows: Among them, LOA j It represents the average value of the longitudinal abnormality of the jth cell of the power battery, V ij Represents the cell voltage of the jth cell at the i-th time, V imid It represents the median of all cell voltages at the i-th moment, and n represents the number of cells in the power battery.

10. The vehicle power battery fault diagnosis method based on hierarchical identification according to claim 8, characterized in that: The steps of determining whether the longitudinal abnormal mean value is abnormal include: For each charging segment / discharging segment, a corresponding standard interval is determined using the three sigma law; Compare the longitudinal anomaly average value calculated using the charging segment / discharging segment with the standard interval: If the longitudinal abnormal mean value exceeds the standard interval, the longitudinal abnormal mean value is considered abnormal.

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